Run download_data.Rmd and percentage_of_regional_richness.Rmd First!

city_data
fetch_city_data_for <- function(pool_name, include_city_name = F, include_pool_size = T) {
  results_filename <- paste(paste(pool_name, 'city', 'richness', 'intercept', sep = "_"), "csv", sep = ".")
  results <- read_csv(results_filename)
  
  joined <- left_join(city_data, results)
  
  pool_size_col_name <- paste(pool_name, 'pool', 'size', sep = "_")
  
  required_columns <- c("population_growth", "rainfall_monthly_min", "rainfall_annual_average", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "temperature_monthly_max", "happiness_negative_effect", "happiness_positive_effect", "happiness_future_life", "number_of_biomes", "realm", "biome_name", "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", "city_includes_estuary", "region_20km_average_pop_density", "region_50km_average_pop_density", "region_100km_average_pop_density", "city_max_pop_density", "city_average_pop_density", "mean_population_exposure_topm2_5_2019", "region_20km_cultivated", "region_20km_urban", "region_50km_cultivated", "region_50km_urban", "region_100km_cultivated", "region_100km_urban", "region_20km_elevation_delta", "region_20km_mean_elevation", "region_50km_elevation_delta", "region_50km_mean_elevation", "region_100km_elevation_delta", "region_100km_mean_elevation", "city_elevation_delta", "city_mean_elevation", "urbanPshrubs", "permanent_water", "open_forest", "herbaceous_wetland", "herbaceous_vegetation", "cultivated", "closed_forest", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_streets", "percentage_urban_area_as_open_public_spaces_and_streets", "percentage_urban_area_as_open_public_spaces", "city_gdp_per_population", "city_ndvi", "city_ssm", "city_susm", "region_20km_ndvi", "region_20km_ssm", "region_20km_susm", "region_50km_ndvi", "region_50km_ssm", "region_50km_susm", "region_100km_ndvi", "region_100km_ssm", "region_100km_susm", "city_percentage_protected", "region_20km_percentage_protected", "region_50km_percentage_protected", "region_100km_percentage_protected")
  
  if (include_pool_size) {
    required_columns <- append(c(pool_size_col_name), required_columns)
  }
  
  if (include_city_name) {
    required_columns <- append(c("name"), required_columns)
  }
  
  required_columns <- append(c("response"), required_columns)
  
  joined[,required_columns]
}
merlin_city_data <- fetch_city_data_for('merlin')
merlin_city_data
library(car)
Loading required package: carData

Attaching package: ‘car’

The following object is masked from ‘package:dplyr’:

    recode

The following object is masked from ‘package:purrr’:

    some

The following object is masked from ‘package:boot’:

    logit
merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
merlin_city_data_fixed

source('./random_forest_selection_functions.R')
scale_parameter_name <- function(scale, postscript) {
  paste('region', paste(scale, 'km', sep = ''), postscript, sep = '_')  
}

scale_parameters <- function(postscript) {
  c(scale_parameter_name(20, postscript), scale_parameter_name(50, postscript), scale_parameter_name(100, postscript))
}

scales_parameters_without <- function(scale_to_exclude, postscript) {
  scales <- scale_parameters(postscript)
  scales[scales != scale_parameter_name(scale_to_exclude, postscript)]
}

select_scales <- function(urban, cultivated, elevation_delta, mean_elevation, average_pop_density, includes_estuary, ssm, susm, ndvi, percentage_protected) {
  append(
    append(
      append(
        append(
          scales_parameters_without(scale_to_exclude = urban, postscript = 'urban'),
          scales_parameters_without(scale_to_exclude = cultivated, postscript = 'cultivated')
        ),
        append(
          scales_parameters_without(scale_to_exclude = elevation_delta, postscript = 'elevation_delta'),
          scales_parameters_without(scale_to_exclude = mean_elevation, postscript = 'mean_elevation')
        )
      ),
      append(
        append(
          scales_parameters_without(scale_to_exclude = average_pop_density, postscript = 'average_pop_density'),
          scales_parameters_without(scale_to_exclude = includes_estuary, postscript = 'includes_estuary')
        ),
        append(
          scales_parameters_without(scale_to_exclude = ssm, postscript = 'ssm'),
          scales_parameters_without(scale_to_exclude = susm, postscript = 'susm')
        )
      )
    ),
    append(
      scales_parameters_without(scale_to_exclude = ndvi, postscript = 'ndvi'),
      scales_parameters_without(scale_to_exclude = percentage_protected, postscript = 'percentage_protected')
    )
  )
}
select_scales(urban = 20, cultivated = 100, elevation_delta = 20, mean_elevation = 100, average_pop_density = NA, includes_estuary = NA, ssm = 20, susm = 20, ndvi = 100, percentage_protected = NA)

select_scales(urban = , cultivated = , elevation_delta = , mean_elevation = , average_pop_density = , includes_estuary = , ssm = , susm = , ndvi =, percentage_protected = )

select_variables_from_random_forest(merlin_city_data_fixed)
exclude_merlin <- !names(merlin_city_data_fixed) %in% select_scales(urban = 20, cultivated = 100, elevation_delta = 50, mean_elevation = 20, average_pop_density = 50, includes_estuary = NA, ssm = 100, susm = 50, ndvi = 20, percentage_protected = 50)

merlin_city_data_fixed_single_scale <- merlin_city_data_fixed[,exclude_merlin]
merlin_city_data_fixed_single_scale
select_variables_from_random_forest(merlin_city_data_fixed_single_scale)
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density", "region_50km_average_pop_density")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density", "region_50km_average_pop_density", "rainfall_annual_average")])

“merlin_pool_size”, “biome_name”, “realm”

birdlife_city_data <- fetch_city_data_for('birdlife')
birdlife_city_data

birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)
birdlife_city_data_fixed
select_variables_from_random_forest(birdlife_city_data_fixed)
exclude_birdlife <- !names(birdlife_city_data_fixed) %in% select_scales(urban = 100, cultivated = 100, elevation_delta = 20, mean_elevation = 100, average_pop_density = 20, includes_estuary = NA, ssm = 50, susm = 100, ndvi = 100, percentage_protected = 100)

birdlife_city_data_fixed_single_scale <- birdlife_city_data_fixed[,exclude_birdlife]
birdlife_city_data_fixed_single_scale
select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm", "region_100km_ndvi")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm", "region_100km_ndvi", "happiness_future_life")])

“population_growth”, “region_50km_ssm”, “birdlife_pool_size”

So….
Merlin: “merlin_pool_size”, “biome_name”, “realm” Birdlife: “population_growth”, “region_50km_ssm”, “birdlife_pool_size”
r ggplot(merlin_city_data_fixed, aes(x = merlin_pool_size, y = response, color = realm)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
`geom_smooth()` using formula 'y ~ x'
r ggplot(merlin_city_data_fixed, aes(x = merlin_pool_size, y = response, color = biome_name)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
`geom_smooth()` using formula 'y ~ x'
r ggplot(birdlife_city_data_fixed, aes(x = birdlife_pool_size, y = response, color = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
`geom_smooth()` using formula 'y ~ x'
r ggplot(birdlife_city_data_fixed, aes(x = birdlife_pool_size, y = response, color = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
`geom_smooth()` using formula 'y ~ x'
r ggplot(merlin_city_data_fixed, aes(y = response, x = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F)
`geom_smooth()` using formula 'y ~ x'
r ggplot(birdlife_city_data_fixed, aes(y = response, x = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F)
`geom_smooth()` using formula 'y ~ x'
r ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F)
`geom_smooth()` using formula 'y ~ x'
r ggplot(birdlife_city_data_fixed, aes(y = response, x = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F)
`geom_smooth()` using formula 'y ~ x'

Try Modelling

library(boot)
merlin_city_data_fixed_no_boreal <- merlin_city_data_fixed[merlin_city_data_fixed$biome_name != 'Boreal Forests/Taiga',]
birdlife_city_data_fixed_no_boreal <- birdlife_city_data_fixed[birdlife_city_data_fixed$biome_name != 'Boreal Forests/Taiga',]
test_model <- function(data, formula) {
  fit <- glm(formula, data = data)
  
  cv.glm(data, fit)$delta

  print(paste("R2", with(summary(fit), 1 - deviance/null.deviance)))
  print(paste("CV Delta", cv.glm(data, fit)$delta))
}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size)
[1] "R2 0.285894381786357"
[1] "CV Delta 13.2924069067549" "CV Delta 13.290989425668" 
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size)
[1] "R2 0.132747072834318"
[1] "CV Delta 5.61376539055778" "CV Delta 5.61321468528147"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + realm)
[1] "R2 0.355479718662977"
[1] "CV Delta 13.1013113338907" "CV Delta 13.0956030666681"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + realm)
[1] "R2 0.215771844466201"
[1] "CV Delta 5.38032952583311" "CV Delta 5.37866865996081"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + biome_name)
[1] "R2 0.370210675877385"
[1] "CV Delta 13.3828176878773" "CV Delta 13.3745769694536"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + biome_name)
[1] "R2 0.223013658291514"
[1] "CV Delta 5.9146455418679"  "CV Delta 5.91040383901086"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + biome_name + realm)
[1] "R2 0.404911112981243"
[1] "CV Delta 14.2088898476971" "CV Delta 14.1942054055947"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + biome_name + realm)
[1] "R2 0.282011390214033"
[1] "CV Delta 5.61291700874678" "CV Delta 5.6084158772121" 
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth)
[1] "R2 0.286929092142329"
[1] "CV Delta 13.5753866440016" "CV Delta 13.5727869610531"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth)
[1] "R2 0.134196770612853"
[1] "CV Delta 5.73874002430138" "CV Delta 5.73766284305409"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm)
[1] "R2 0.290846971284311"
[1] "CV Delta 13.7387732818639" "CV Delta 13.7352820567926"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm)
[1] "R2 0.151946781581196"
[1] "CV Delta 5.6860363390934"  "CV Delta 5.68473754059936"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.409838701070553"
[1] "CV Delta 14.5182790115777" "CV Delta 14.5020009668314"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.287701159199758"
[1] "CV Delta 5.81750910931357" "CV Delta 5.81199090164147"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + biome_name + realm)
[1] "R2 0.408007484714431"
[1] "CV Delta 14.3038421650361" "CV Delta 14.2885697715507"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + biome_name + realm)
[1] "R2 0.287698318614449"
[1] "CV Delta 5.66636239287125" "CV Delta 5.66147724370065"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + realm)
[1] "R2 0.359678173824136"
[1] "CV Delta 13.2441153115841" "CV Delta 13.2375767587302"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + realm)
[1] "R2 0.226422719550459"
[1] "CV Delta 5.38608965982802" "CV Delta 5.38414802481297"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + biome_name)
[1] "R2 0.370916016196228"
[1] "CV Delta 13.5692480406954" "CV Delta 13.5602234710036"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + biome_name)
[1] "R2 0.229572509441164"
[1] "CV Delta 5.99492133909033" "CV Delta 5.9901231822001" 
AIC(
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + biome_name),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + biome_name),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + realm)
)
AIC(
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + realm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + biome_name),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + biome_name + realm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + region_50km_ssm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
)
merlin_city_data_named <- fetch_city_data_for('merlin', T)

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
birdlife_city_data_named <- fetch_city_data_for('birdlife', T)

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
merlin.fit <- glm(data = merlin_city_data_fixed, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
plot(merlin.fit)
Warning: not plotting observations with leverage one:
  113

jpeg("city_effect_merlin_model.jpg")
par(mfrow=c(2, 2))
plot(merlin.fit)
Warning: not plotting observations with leverage one:
  113
dev.off()
null device 
          1 
merlin_city_data_fixed[c(24, 30, 31),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "merlin_pool_size")]
birdlife_city_data_fixed[c(24, 30, 31),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "birdlife_pool_size")]
merlin_city_data_named$name[c(24, 30, 31)]
[1] "Casablanca" "Colombo"    "Curitiba"  
birdlife_city_data_named$name[c(24, 30, 31)]
[1] "Casablanca" "Colombo"    "Curitiba"  
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Mediterranean Forests, Woodlands & Scrub'])
[1] 240.5333
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Palearctic'])
[1] 216.3846
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
[1] 300.6136
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Indomalayan'])
[1] 282.775
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
[1] 300.6136
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Neotropic'])
[1] 334.3333
test_model(merlin_city_data_fixed[-c(24, 30, 31, 113),], response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.479991092783881"
[1] "CV Delta 10.665095263287"  "CV Delta 10.6515112719514"
birdlife.fit <- glm(data = birdlife_city_data_fixed, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
plot(birdlife.fit)
Warning: not plotting observations with leverage one:
  113

jpeg("city_effect_birdlife_model.jpg")
par(mfrow=c(2, 2))
plot(birdlife.fit)
Warning: not plotting observations with leverage one:
  113
dev.off()
null device 
          1 
birdlife_city_data_fixed[c(16, 30, 53),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "birdlife_pool_size")]
merlin_city_data_fixed[c(16, 30, 53),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "merlin_pool_size")]
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Temperate Broadleaf & Mixed Forests'])
[1] 232.7188
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Palearctic'])
[1] 211.9231
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
[1] 364.2727
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Indomalayan'])
[1] 337.75
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Tropical & Subtropical Dry Broadleaf Forests'])
[1] 310.2143
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Indomalayan'])
[1] 337.75
birdlife_city_data_named$name[c(16, 30, 53)]
[1] "Birmingham" "Colombo"    "Hyderabad" 
test_model(birdlife_city_data_fixed[-c(16, 30, 53, 113),], response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.32316086964121"
[1] "CV Delta 4.41986102308419" "CV Delta 4.41468426423238"
But can we order cities based on how good they are for biodiversity?
merlin_city_data_fixed$residuals <- resid(merlin.fit)
birdlife_city_data_fixed$residuals <- resid(birdlife.fit)
ggplot(merlin_city_data_fixed, aes(y = response, x = residuals)) + geom_point() + geom_smooth(method = "lm", se = F)
`geom_smooth()` using formula 'y ~ x'

cor(merlin_city_data_fixed$response, merlin_city_data_fixed$residuals)
[1] 0.7665527
ggplot(birdlife_city_data_fixed, aes(y = response, x = residuals)) + geom_point() + geom_smooth(method = "lm", se = F)
`geom_smooth()` using formula 'y ~ x'

cor(birdlife_city_data_fixed$response, birdlife_city_data_fixed$residuals)
[1] 0.8398848
ordered_cities <- data.frame(
  ranked_performance = 1:nrow(merlin_city_data_named),
  merlin_base_response = merlin_city_data_named$name[order(-merlin_city_data$response)],
  birdlife_base_response = merlin_city_data_named$name[order(-birdlife_city_data$response)],
  merlin_model_residuals = merlin_city_data_named$name[order(-merlin_city_data$residuals)],
  birdlife_model_residuals = merlin_city_data_named$name[order(-birdlife_city_data$residuals)]
)
ordered_cities
write_csv(ordered_cities, "city_effect_residuals.csv")
What is going on with the response?
library(ggrepel)
merlin_city_data_fixed$name <- merlin_city_data_named$name
plot_merlin_poolsize <- ggplot(merlin_city_data_fixed, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size")
plot_merlin_poolsize
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 123 unlabeled data points (too many overlaps). Consider increasing max.overlaps

birdlife_city_data_fixed$name <- birdlife_city_data_named$name
plot_birdlife_poolsize <- ggplot(birdlife_city_data_fixed, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size")
plot_birdlife_poolsize
`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 114 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Summary of models
summary(merlin.fit)

Call:
glm(formula = response ~ merlin_pool_size + population_growth + 
    region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-7.5836  -1.9802  -0.2806   1.4883  16.1666  

Coefficients:
                                                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                         2.558383   4.200653   0.609   0.5437    
merlin_pool_size                                                   -0.027339   0.003575  -7.647 6.55e-12 ***
population_growth                                                   0.003068   0.005114   0.600   0.5497    
region_50km_ssm                                                    -0.053173   0.072982  -0.729   0.4677    
biome_nameDeserts & Xeric Shrublands                                4.605968   3.868850   1.191   0.2363    
biome_nameFlooded Grasslands & Savannas                             0.525908   4.481070   0.117   0.9068    
biome_nameMangroves                                                 8.441618   4.591210   1.839   0.0685 .  
biome_nameMediterranean Forests, Woodlands & Scrub                  4.145677   3.732373   1.111   0.2690    
biome_nameMontane Grasslands & Shrublands                           5.023979   4.774921   1.052   0.2949    
biome_nameTemperate Broadleaf & Mixed Forests                       4.686888   3.622288   1.294   0.1983    
biome_nameTemperate Conifer Forests                                 4.317564   4.479751   0.964   0.3372    
biome_nameTemperate Grasslands, Savannas & Shrublands               5.637210   4.037582   1.396   0.1653    
biome_nameTropical & Subtropical Coniferous Forests                 7.544896   4.609955   1.637   0.1044    
biome_nameTropical & Subtropical Dry Broadleaf Forests              4.834888   3.977950   1.215   0.2267    
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands  7.209246   4.186447   1.722   0.0877 .  
biome_nameTropical & Subtropical Moist Broadleaf Forests            4.084507   3.830228   1.066   0.2885    
realmAustralasia                                                   -0.633465   2.622994  -0.242   0.8096    
realmIndomalayan                                                    1.301503   1.655451   0.786   0.4334    
realmNearctic                                                       2.083151   1.879997   1.108   0.2701    
realmNeotropic                                                      2.585444   1.767718   1.463   0.1463    
realmPalearctic                                                    -0.323305   1.843458  -0.175   0.8611    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 12.50991)

    Null deviance: 2469.6  on 136  degrees of freedom
Residual deviance: 1451.1  on 116  degrees of freedom
AIC: 756.13

Number of Fisher Scoring iterations: 2
summary(birdlife.fit)

Call:
glm(formula = response ~ birdlife_pool_size + population_growth + 
    region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-5.1697  -1.2864  -0.2075   0.8359   9.4606  

Coefficients:
                                                                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                         3.639e+00  2.811e+00   1.295   0.1980    
birdlife_pool_size                                                 -1.298e-02  2.705e-03  -4.798 4.82e-06 ***
population_growth                                                  -7.172e-05  3.334e-03  -0.022   0.9829    
region_50km_ssm                                                    -4.483e-02  4.664e-02  -0.961   0.3385    
biome_nameDeserts & Xeric Shrublands                                3.037e+00  2.499e+00   1.215   0.2267    
biome_nameFlooded Grasslands & Savannas                             5.831e-01  2.904e+00   0.201   0.8412    
biome_nameMangroves                                                 3.282e+00  2.980e+00   1.101   0.2730    
biome_nameMediterranean Forests, Woodlands & Scrub                  2.506e+00  2.415e+00   1.038   0.3015    
biome_nameMontane Grasslands & Shrublands                           2.011e+00  3.094e+00   0.650   0.5170    
biome_nameTemperate Broadleaf & Mixed Forests                       3.067e+00  2.345e+00   1.308   0.1934    
biome_nameTemperate Conifer Forests                                 4.456e+00  2.907e+00   1.533   0.1280    
biome_nameTemperate Grasslands, Savannas & Shrublands               4.342e+00  2.616e+00   1.660   0.0996 .  
biome_nameTropical & Subtropical Coniferous Forests                 3.878e+00  2.988e+00   1.298   0.1969    
biome_nameTropical & Subtropical Dry Broadleaf Forests              3.043e+00  2.570e+00   1.184   0.2389    
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands  1.675e+00  2.724e+00   0.615   0.5400    
biome_nameTropical & Subtropical Moist Broadleaf Forests            1.819e+00  2.482e+00   0.733   0.4651    
realmAustralasia                                                   -1.882e+00  1.700e+00  -1.107   0.2706    
realmIndomalayan                                                   -8.270e-01  1.076e+00  -0.769   0.4436    
realmNearctic                                                      -2.992e+00  1.228e+00  -2.437   0.0163 *  
realmNeotropic                                                     -6.657e-01  1.143e+00  -0.582   0.5615    
realmPalearctic                                                    -2.961e+00  1.225e+00  -2.417   0.0172 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 5.26285)

    Null deviance: 865.45  on 136  degrees of freedom
Residual deviance: 610.49  on 116  degrees of freedom
AIC: 637.51

Number of Fisher Scoring iterations: 2
Review anovas
birdlife.biome.anovoa <- aov(response ~ biome_name, data=birdlife_city_data_fixed)
summary(birdlife.biome.anovoa)
             Df Sum Sq Mean Sq F value Pr(>F)
biome_name   12   98.7   8.225    1.33   0.21
Residuals   124  766.7   6.183               
merlin.biome.anovoa <- aov(response ~ biome_name, data=merlin_city_data_fixed)
summary(merlin.biome.anovoa)
             Df Sum Sq Mean Sq F value Pr(>F)
biome_name   12  212.3   17.69   0.972  0.479
Residuals   124 2257.3   18.20               
birdlife.realm.anovoa <- aov(response ~ realm, data=birdlife_city_data_fixed)
summary(birdlife.realm.anovoa)
             Df Sum Sq Mean Sq F value Pr(>F)
realm         5    0.0   0.000       0      1
Residuals   131  865.4   6.606               
merlin.realm.anovoa <- aov(response ~ realm, data=merlin_city_data_fixed)
summary(merlin.realm.anovoa)
             Df Sum Sq Mean Sq F value Pr(>F)
realm         5      0    0.00       0      1
Residuals   131   2470   18.85               
interaction.plot(merlin_city_data_fixed$realm, merlin_city_data_fixed$biome_name, merlin_city_data_fixed$response)


meriin.addative.anova <- aov(response ~ biome_name + realm, data=merlin_city_data_fixed) 
summary(meriin.addative.anova)
             Df Sum Sq Mean Sq F value Pr(>F)
biome_name   12  212.3  17.692   0.938  0.511
realm         5   13.9   2.785   0.148  0.980
Residuals   119 2243.4  18.852               
interaction.plot(merlin_city_data_fixed$realm, merlin_city_data_fixed$biome_name, merlin_city_data_fixed$response)


meriin.interaction.anova <- aov(response ~ biome_name * realm, data=merlin_city_data_fixed) 
summary(meriin.interaction.anova)
                  Df Sum Sq Mean Sq F value Pr(>F)
biome_name        12  212.3  17.692   0.890  0.559
realm              5   13.9   2.785   0.140  0.983
biome_name:realm  13  136.3  10.487   0.528  0.903
Residuals        106 2107.1  19.878               
ggplot(merlin_city_data_fixed, aes(x = response, y = realm)) + geom_boxplot()

library("stringr")     
ggplot(merlin_city_data_fixed, aes(x = response, y = biome_name)) + 
  geom_boxplot() + 
  facet_wrap(~ realm, scales = "free") +
  theme(text = element_text(size = 30), legend.text=element_text(size=20)) +
  scale_y_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(birdlife_city_data_fixed, aes(x = response, y = biome_name)) + 
  geom_boxplot() + 
  facet_wrap(~ realm, scales = "free") +
  theme(text = element_text(size = 30), legend.text=element_text(size=20)) +
  scale_y_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm, color = realm)) + geom_point() + geom_smooth(method = "lm", se = F)
`geom_smooth()` using formula 'y ~ x'

ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm, color = biome_name)) + geom_point() + geom_smooth(method = "lm", se = F)
`geom_smooth()` using formula 'y ~ x'

test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.409838701070553"
[1] "CV Delta 14.5182790115777" "CV Delta 14.5020009668315"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
[1] "R2 0.287701159199758"
[1] "CV Delta 5.81750910931357" "CV Delta 5.81199090164147"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm *  biome_name * realm)
[1] "R2 0.519307720872686"
[1] "CV Delta 13363.0206446473" "CV Delta 13264.8507709478"
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm * biome_name * realm)
[1] "R2 0.432634735857367"
[1] "CV Delta 11005.7362693327" "CV Delta 10924.8441449411"
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm * biome_name * realm)
[1] "R2 0.519293181227938"
[1] "CV Delta 13625.5384965618" "CV Delta 13525.4374613711"
min(merlin_city_data$response)
[1] -9.204166
max(merlin_city_data$response)
[1] 18.43521
min(birdlife_city_data$response)
[1] -4.910185
max(birdlife_city_data$response)
[1] 10.45823
Maybe the either pool is missing the random pool sizes?
either_city_data <- fetch_city_data_for('either')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"
either_city_data

either_city_data_fixed <- rfImpute(response ~ ., either_city_data)
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.843    95.24 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.704    92.50 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.006    98.43 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.796    94.32 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.824    94.87 |
either_city_data_fixed
select_variables_from_random_forest(either_city_data_fixed)
 [1] "either_pool_size"                                        "region_50km_ssm"                                         "population_growth"                                      
 [4] "region_100km_ssm"                                        "realm"                                                   "region_100km_cultivated"                                
 [7] "region_20km_average_pop_density"                         "region_100km_susm"                                       "region_20km_ssm"                                        
[10] "region_50km_cultivated"                                  "temperature_monthly_min"                                 "region_50km_elevation_delta"                            
[13] "region_50km_average_pop_density"                         "shrubs"                                                  "city_ssm"                                               
[16] "region_20km_susm"                                        "permanent_water"                                         "region_100km_average_pop_density"                       
[19] "region_50km_susm"                                        "rainfall_monthly_min"                                    "mean_population_exposure_to_pm2_5_2019"                 
[22] "region_20km_cultivated"                                  "biome_name"                                              "share_of_population_within_400m_of_open_space"          
[25] "region_20km_elevation_delta"                             "rainfall_monthly_max"                                    "temperature_annual_average"                             
[28] "city_elevation_delta"                                    "percentage_urban_area_as_open_public_spaces_and_streets" "happiness_positive_effect"                              
[31] "city_susm"                                               "city_average_pop_density"                                "region_100km_elevation_delta"                           
[34] "region_20km_ndvi"                                        "region_50km_ndvi"                                        "city_max_pop_density"                                   
[37] "city_mean_elevation"                                     "percentage_urban_area_as_open_public_spaces"             "region_20km_urban"                                      
[40] "region_100km_urban"                                      "temperature_monthly_max"                                 "region_20km_percentage_protected"                       
[43] "region_50km_percentage_protected"                        "rainfall_annual_average"                                 "herbaceous_wetland"                                     
[46] "region_50km_mean_elevation"                              "region_20km_mean_elevation"                              "region_50km_urban"                                      
[49] "city_ndvi"                                               "region_100km_mean_elevation"                             "cultivated"                                             
[52] "city_percentage_protected"                               "happiness_future_life"                                   "region_100km_ndvi"                                      
[55] "happiness_negative_effect"                               "open_forest"                                             "urban"                                                  
[58] "herbaceous_vegetation"                                   "closed_forest"                                           "percentage_urban_area_as_streets"                       
select_variables_from_random_forest(either_city_data_fixed_single_scale)
 [1] "either_pool_size"                                        "region_50km_ssm"                                         "population_growth"                                      
 [4] "region_100km_cultivated"                                 "realm"                                                   "region_20km_average_pop_density"                        
 [7] "region_100km_susm"                                       "city_ssm"                                                "temperature_monthly_min"                                
[10] "shrubs"                                                  "region_50km_elevation_delta"                             "biome_name"                                             
[13] "permanent_water"                                         "rainfall_monthly_min"                                    "share_of_population_within_400m_of_open_space"          
[16] "percentage_urban_area_as_open_public_spaces"             "mean_population_exposure_to_pm2_5_2019"                  "temperature_annual_average"                             
[19] "city_average_pop_density"                                "percentage_urban_area_as_open_public_spaces_and_streets" "happiness_positive_effect"                              
[22] "city_mean_elevation"                                     "region_20km_ndvi"                                        "city_ndvi"                                              
[25] "rainfall_annual_average"                                 "city_susm"                                               "region_50km_mean_elevation"                             
[28] "temperature_monthly_max"                                 "open_forest"                                             "happiness_negative_effect"                              
[31] "herbaceous_vegetation"                                   "closed_forest"                                           "percentage_urban_area_as_streets"                       
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])
[1] "Mean  4.69266103594363 , SD:  0.0510981914922011 , Mean + SD:  4.74375922743583"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm")])
[1] "Mean  4.31336571084706 , SD:  0.0477119635734014 , Mean + SD:  4.36107767442046"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth")])
[1] "Mean  3.84257546120148 , SD:  0.0583801219753036 , Mean + SD:  3.90095558317678"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated")])
[1] "Mean  3.97972587397746 , SD:  0.0610228479845339 , Mean + SD:  4.04074872196199"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm")])
[1] "Mean  3.71041026502135 , SD:  0.0604558222228108 , Mean + SD:  3.77086608724416"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density")])
[1] "Mean  3.66339757092933 , SD:  0.0663715820467887 , Mean + SD:  3.72976915297612"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm")])
[1] "Mean  3.85235242406019 , SD:  0.0679378104226804 , Mean + SD:  3.92029023448287"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm")])
[1] "Mean  4.00570750061168 , SD:  0.0578080868110856 , Mean + SD:  4.06351558742276"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min")])
[1] "Mean  3.92441683942565 , SD:  0.0710013584983004 , Mean + SD:  3.99541819792395"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs")])
[1] "Mean  3.93883898944311 , SD:  0.0771381589247996 , Mean + SD:  4.01597714836791"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta")])
[1] "Mean  3.97887705384933 , SD:  0.0641411182593364 , Mean + SD:  4.04301817210867"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name")])
[1] "Mean  4.12424218224607 , SD:  0.0654822740801436 , Mean + SD:  4.18972445632622"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water")])
[1] "Mean  4.13307783362438 , SD:  0.0520988790244683 , Mean + SD:  4.18517671264885"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min")])
[1] "Mean  4.14763343564045 , SD:  0.0712446152059106 , Mean + SD:  4.21887805084636"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space")])
[1] "Mean  4.1661488519461 , SD:  0.0659605155787169 , Mean + SD:  4.23210936752482"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces")])
[1] "Mean  4.22755212132415 , SD:  0.0750663802220243 , Mean + SD:  4.30261850154617"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019")])
[1] "Mean  4.23552884460268 , SD:  0.0745476529202964 , Mean + SD:  4.31007649752298"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average")])
[1] "Mean  4.26508667731091 , SD:  0.0773348834939869 , Mean + SD:  4.3424215608049"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density")])
[1] "Mean  4.28326756101597 , SD:  0.0755017970060168 , Mean + SD:  4.35876935802199"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density", "percentage_urban_area_as_open_public_spaces_and_streets")])
[1] "Mean  4.34654977254916 , SD:  0.0660243511076323 , Mean + SD:  4.41257412365679"
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density", "percentage_urban_area_as_open_public_spaces_and_streets", "happiness_positive_effect")])
[1] "Mean  4.35777574870304 , SD:  0.0710294002562521 , Mean + SD:  4.42880514895929"

“either_pool_size”, “region_50km_ssm”, “population_growth”, “region_100km_cultivated”, “realm”, “region_20km_average_pop_density”

test_model(either_city_data_fixed, response ~ either_pool_size)
[1] "R2 0.200828561945557"
[1] "CV Delta 4.17185408588075" "CV Delta 4.17145621339379"
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm)
[1] "R2 0.218556419678468"
[1] "CV Delta 4.1385005044194"  "CV Delta 4.13789124054347"
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth)
[1] "R2 0.218651209733749"
[1] "CV Delta 4.24526922992079" "CV Delta 4.24423824005575"
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated)
[1] "R2 0.240163336907313"
[1] "CV Delta 4.1925470002567"  "CV Delta 4.19130237140134"
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm)
[1] "R2 0.337735042304765"
[1] "CV Delta 3.90417611638399" "CV Delta 3.90212025458098"
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm + region_20km_average_pop_density)
[1] "R2 0.348335268589981"
[1] "CV Delta 3.89011193891222" "CV Delta 3.88782718460849"
cor(either_city_data_fixed$residuals, either_city_data_fixed$response)
[1] 0.8072575

mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Temperate Broadleaf & Mixed Forests'])
[1] 293.4062
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Palearctic'])
[1] 274.0769
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
[1] 428.25
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Indomalayan'])
[1] 395.225
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Tropical & Subtropical Dry Broadleaf Forests'])
[1] 379.5
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Indomalayan'])
[1] 395.225
birdlife_city_data_named$name[c(16, 30, 53)]
[1] "Birmingham" "Colombo"    "Hyderabad" 
plot(either.fit.2)
Warning: not plotting observations with leverage one:
  113

Use cross validation and dropping terms to find best model
predict <- NULL
error <- NULL
for(i in 1:nrow(merlin_city_data_fixed_no_boreal)) {
    fit <- glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal[-c(i),])
    predict[i] <- predict(fit, newdata=merlin_city_data_fixed_no_boreal[c(i),])
    error[i] <- (predict[i] - merlin_city_data_fixed_no_boreal$response[c(i)])^2
}
mean(error)
[1] 14.51828
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 14.51828

– Can we drop one?

cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.8059
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.46984
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 14.39671
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 14.30384
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 20.17595

– drop populaion_growth (14.30384)

– can we drop another?

cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.56925
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.24412
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 14.20889
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 20.17595

– drop ssm (14.20889)

– can we drop another?

cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.38282
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.10131
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 20.56763

– drop biome (13.10131)

cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.29241
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 19.48513
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size * realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
[1] 13.85502
– best model with merlin is pool size + realm (CV error 13.10131)
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.817509

– can we drop a variable?

cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 6.1237
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.492536
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.764969
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.666362
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ population_growth + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 7.061169

– drop biome (5.492536)

cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.686036
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.499406
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.38609
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ population_growth + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 6.690224

– drop population growth (5.38609)

cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.577701
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.38033
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 6.637624

– drop ssm (5.38033)

cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 5.613765
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
[1] 6.746644
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size * realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
– so best model with birdlife is pool size + realm
ggplot(merlin_city_data_fixed_no_boreal, aes(x = merlin_pool_size, y = response)) + geom_point() + geom_smooth(method = "glm", se = F) + facet_wrap(~ realm)
`geom_smooth()` using formula 'y ~ x'

ggplot(birdlife_city_data_fixed_no_boreal, aes(x = birdlife_pool_size, y = response)) + geom_point() + geom_smooth(method = "glm", se = F) + facet_wrap(~ realm)
`geom_smooth()` using formula 'y ~ x'

birdlife.fit <- glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + realm)
summary(birdlife.fit)

Call:
glm(formula = response ~ birdlife_pool_size + realm, data = birdlife_city_data_fixed_no_boreal)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-5.9240  -1.2855  -0.3158   0.8601   9.4227  

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         5.724170   1.182215   4.842 3.61e-06 ***
birdlife_pool_size -0.014963   0.002513  -5.955 2.31e-08 ***
realmAustralasia   -1.504499   1.508076  -0.998  0.32033    
realmIndomalayan   -0.670291   0.785239  -0.854  0.39490    
realmNearctic      -1.963875   0.918121  -2.139  0.03432 *  
realmNeotropic     -0.288102   0.832533  -0.346  0.72987    
realmPalearctic    -2.474092   0.891147  -2.776  0.00632 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 5.210381)

    Null deviance: 857.07  on 135  degrees of freedom
Residual deviance: 672.14  on 129  degrees of freedom
AIC: 619.25

Number of Fisher Scoring iterations: 2
with(summary(birdlife.fit), 1 - deviance/null.deviance)
[1] 0.2157718
plot(birdlife.fit)

ggplot(birdlife_city_data_fixed_no_boreal, aes(x = birdlife_pool_size, y = response)) + 
  geom_point() + 
  geom_smooth(method = "glm", se = F) +
  geom_text(aes(label = name), data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], size = 3, position = "dodge", vjust = "inward", hjust = "inward", color = "red", angle=-15) +
  geom_point(data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], color = "red") +
  facet_wrap(~ realm) +
  theme_bw()
`geom_smooth()` using formula 'y ~ x'
Warning: Width not defined. Set with `position_dodge(width = ?)`

merlin.fit <- glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + realm)
summary(merlin.fit)

Call:
glm(formula = response ~ merlin_pool_size + realm, data = merlin_city_data_fixed_no_boreal)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-8.5389  -1.7132  -0.5164   1.3898  16.2335  

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.823312   1.406157   5.564 1.46e-07 ***
merlin_pool_size -0.028685   0.003401  -8.434 5.79e-14 ***
realmAustralasia -1.923665   2.294340  -0.838    0.403    
realmIndomalayan  0.288224   1.193798   0.241    0.810    
realmNearctic     1.499468   1.327678   1.129    0.261    
realmNeotropic    1.767199   1.293308   1.366    0.174    
realmPalearctic  -1.490884   1.214536  -1.228    0.222    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 12.28537)

    Null deviance: 2458.9  on 135  degrees of freedom
Residual deviance: 1584.8  on 129  degrees of freedom
AIC: 735.91

Number of Fisher Scoring iterations: 2
with(summary(merlin.fit), 1 - deviance/null.deviance)
[1] 0.3554797
plot(merlin.fit)

ggplot(merlin_city_data_fixed_no_boreal, aes(x = merlin_pool_size, y = response)) + 
  geom_point(size = 1) + 
  geom_smooth(method = "glm", se = F) +
  geom_text(aes(label = name), data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], size = 3, position = "dodge", vjust = "inward", hjust = "inward", color = "red", angle=-15) +
  geom_point(data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], color = "red") +
  facet_wrap(~ realm) +
  theme_bw()
`geom_smooth()` using formula 'y ~ x'
Warning: Width not defined. Set with `position_dodge(width = ?)`

ggplot(merlin_city_data_fixed_no_boreal, aes(y = response, x = residuals)) + 
  geom_smooth(method = "lm", se = F) +
  geom_point(aes(color = realm)) + 
  geom_text(aes(label = name), data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], size = 4, position = "dodge", vjust = "inward", hjust = "inward") + 
  labs(title = "Merlin", subtitle = paste("Correlation", cor(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$response))) +
  theme_bw()
`geom_smooth()` using formula 'y ~ x'
Warning: Width not defined. Set with `position_dodge(width = ?)`

ggplot(birdlife_city_data_fixed_no_boreal, aes(y = response, x = residuals)) + 
  geom_smooth(method = "lm", se = F, alpha = 0.5) +
  geom_point(aes(color = realm)) + 
  geom_text(aes(label = name), data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], size = 4, position = "dodge", vjust = "inward", hjust = "inward") +
  labs(title = "Birdlife", subtitle = paste("Correlation", cor(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$response))) +
  theme_bw()
`geom_smooth()` using formula 'y ~ x'
Warning: Width not defined. Set with `position_dodge(width = ?)`

summary(birdlife.aov)
                                          Df Sum Sq Mean Sq F value Pr(>F)
birdlife_city_data_fixed_no_boreal$realm   5   0.16  0.0315   0.017      1
Residuals                                130 246.42  1.8956               

merlin.aov <- aov(merlin_city_data_fixed_no_boreal$residuals_of_fit ~ merlin_city_data_fixed_no_boreal$realm)
summary(merlin.aov)
                                        Df Sum Sq Mean Sq F value Pr(>F)
merlin_city_data_fixed_no_boreal$realm   5    0.2    0.04   0.005      1
Residuals                              130 1007.6    7.75               

shapiro.test(birdlife_city_data_fixed_no_boreal$response)

    Shapiro-Wilk normality test

data:  birdlife_city_data_fixed_no_boreal$response
W = 0.93733, p-value = 8.842e-06
shapiro.test(birdlife_city_data_fixed_no_boreal$residuals)

    Shapiro-Wilk normality test

data:  birdlife_city_data_fixed_no_boreal$residuals
W = 0.9341, p-value = 5.274e-06
shapiro.test(birdlife_city_data_fixed_no_boreal$residuals_of_fit)

    Shapiro-Wilk normality test

data:  birdlife_city_data_fixed_no_boreal$residuals_of_fit
W = 0.96887, p-value = 0.003322
shapiro.test(merlin_city_data_fixed_no_boreal$response)

    Shapiro-Wilk normality test

data:  merlin_city_data_fixed_no_boreal$response
W = 0.95308, p-value = 0.0001366
shapiro.test(merlin_city_data_fixed_no_boreal$residuals)

    Shapiro-Wilk normality test

data:  merlin_city_data_fixed_no_boreal$residuals
W = 0.9243, p-value = 1.19e-06
shapiro.test(merlin_city_data_fixed_no_boreal$residuals_of_fit)

    Shapiro-Wilk normality test

data:  merlin_city_data_fixed_no_boreal$residuals_of_fit
W = 0.95023, p-value = 8.081e-05

Variances of groups are NOT significantly different:

bartlett.test(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$realm) 

    Bartlett test of homogeneity of variances

data:  merlin_city_data_fixed_no_boreal$residuals and merlin_city_data_fixed_no_boreal$realm
Bartlett's K-squared = 10.491, df = 5, p-value = 0.06246
leveneTest(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$realm) 
Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   5  1.0579 0.3868
      130               

Variances of groups are NOT significantly different:

bartlett.test(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$realm) 

    Bartlett test of homogeneity of variances

data:  birdlife_city_data_fixed_no_boreal$residuals and birdlife_city_data_fixed_no_boreal$realm
Bartlett's K-squared = 13.866, df = 5, p-value = 0.01648
leveneTest(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$realm) 
Levene's Test for Homogeneity of Variance (center = median)
       Df F value Pr(>F)
group   5  1.1694 0.3277
      130               

Variances of groups are significantly different:

bartlett.test(merlin_city_data_fixed_no_boreal$residuals_of_fit, merlin_city_data_fixed_no_boreal$realm) 

    Bartlett test of homogeneity of variances

data:  merlin_city_data_fixed_no_boreal$residuals_of_fit and merlin_city_data_fixed_no_boreal$realm
Bartlett's K-squared = 71.202, df = 5, p-value = 5.76e-14
leveneTest(merlin_city_data_fixed_no_boreal$residuals_of_fit, merlin_city_data_fixed_no_boreal$realm) 
Levene's Test for Homogeneity of Variance (center = median)
       Df F value    Pr(>F)    
group   5  10.565 1.576e-08 ***
      130                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Variances of groups are significantly different:

bartlett.test(birdlife_city_data_fixed_no_boreal$residuals_of_fit, birdlife_city_data_fixed_no_boreal$realm) 

    Bartlett test of homogeneity of variances

data:  birdlife_city_data_fixed_no_boreal$residuals_of_fit and birdlife_city_data_fixed_no_boreal$realm
Bartlett's K-squared = 47.349, df = 5, p-value = 4.824e-09
leveneTest(birdlife_city_data_fixed_no_boreal$residuals_of_fit, birdlife_city_data_fixed_no_boreal$realm) 
Levene's Test for Homogeneity of Variance (center = median)
       Df F value    Pr(>F)    
group   5  11.458 3.558e-09 ***
      130                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
unique(merlin_city_data_fixed_no_boreal$realm)
[1] Afrotropic  Indomalayan Palearctic  Neotropic   Nearctic    Australasia
Levels: Afrotropic Australasia Indomalayan Nearctic Neotropic Palearctic
for(i in 1:length(realms)) {
  realm <- realms[i]
  delta <- cv.glm(
              data = merlin_city_data_fixed_no_boreal[merlin_city_data_fixed_no_boreal$realm != realm,], 
              glm(
                data = merlin_city_data_fixed_no_boreal[merlin_city_data_fixed_no_boreal$realm != realm,],
                formula = response ~ merlin_pool_size + realm
              )
           )$delta[1]
  print(paste("Exclude Realm CV", realm, delta))
}
[1] "Exclude Realm CV Afrotropic 12.7706233346667"
[1] "Exclude Realm CV Indomalayan 12.9137476664078"
[1] "Exclude Realm CV Palearctic 12.4964911080885"
[1] "Exclude Realm CV Neotropic 12.6929671331165"
[1] "Exclude Realm CV Nearctic 14.8108152865969"
[1] "Exclude Realm CV Australasia 12.8293479262406"
for(i in 1:length(realms)) {
  realm <- realms[i]
  delta <- cv.glm(
              data = birdlife_city_data_fixed_no_boreal[birdlife_city_data_fixed_no_boreal$realm != realm,], 
              glm(
                data = birdlife_city_data_fixed_no_boreal[birdlife_city_data_fixed_no_boreal$realm != realm,],
                formula = response ~ birdlife_pool_size + realm
              )
           )$delta[1]
  print(paste("Exclude Realm CV", realm, delta))
}
[1] "Exclude Realm CV Afrotropic 5.72613070569415"
[1] "Exclude Realm CV Indomalayan 4.93246008728438"
[1] "Exclude Realm CV Palearctic 4.86504674156399"
[1] "Exclude Realm CV Neotropic 5.2626582383154"
[1] "Exclude Realm CV Nearctic 5.87005564586449"
[1] "Exclude Realm CV Australasia 5.49824641265513"
---
title: "R Notebook"
output: html_notebook
---
Run `download_data.Rmd` and `percentage_of_regional_richness.Rmd` First!

```{r}
city_data
```


```{r}
fetch_city_data_for <- function(pool_name, include_city_name = F, include_pool_size = T) {
  results_filename <- paste(paste(pool_name, 'city', 'richness', 'intercept', sep = "_"), "csv", sep = ".")
  results <- read_csv(results_filename)
  
  joined <- left_join(city_data, results)
  
  pool_size_col_name <- paste(pool_name, 'pool', 'size', sep = "_")
  
  required_columns <- c("population_growth", "rainfall_monthly_min", "rainfall_annual_average", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "temperature_monthly_max", "happiness_negative_effect", "happiness_positive_effect", "happiness_future_life", "number_of_biomes", "realm", "biome_name", "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", "city_includes_estuary", "region_20km_average_pop_density", "region_50km_average_pop_density", "region_100km_average_pop_density", "city_max_pop_density", "city_average_pop_density", "mean_population_exposure_topm2_5_2019", "region_20km_cultivated", "region_20km_urban", "region_50km_cultivated", "region_50km_urban", "region_100km_cultivated", "region_100km_urban", "region_20km_elevation_delta", "region_20km_mean_elevation", "region_50km_elevation_delta", "region_50km_mean_elevation", "region_100km_elevation_delta", "region_100km_mean_elevation", "city_elevation_delta", "city_mean_elevation", "urbanPshrubs", "permanent_water", "open_forest", "herbaceous_wetland", "herbaceous_vegetation", "cultivated", "closed_forest", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_streets", "percentage_urban_area_as_open_public_spaces_and_streets", "percentage_urban_area_as_open_public_spaces", "city_gdp_per_population", "city_ndvi", "city_ssm", "city_susm", "region_20km_ndvi", "region_20km_ssm", "region_20km_susm", "region_50km_ndvi", "region_50km_ssm", "region_50km_susm", "region_100km_ndvi", "region_100km_ssm", "region_100km_susm", "city_percentage_protected", "region_20km_percentage_protected", "region_50km_percentage_protected", "region_100km_percentage_protected")
  
  if (include_pool_size) {
    required_columns <- append(c(pool_size_col_name), required_columns)
  }
  
  if (include_city_name) {
    required_columns <- append(c("name"), required_columns)
  }
  
  required_columns <- append(c("response"), required_columns)
  
  joined[,required_columns]
}
```


```{r}
merlin_city_data <- fetch_city_data_for('merlin')
merlin_city_data
```

```{r}
library(randomForest)
library(reshape2)
library(rpart)
library(ggplot2)
library(tidyverse)

library(multcomp)
library(car)
```

```{r}
merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
merlin_city_data_fixed
```

```{r}
ggplot(merlin_city_data_fixed, aes(response)) + geom_histogram(binwidth = 2)
```

```{r}
source('./random_forest_selection_functions.R')
```

```{r}
scale_parameter_name <- function(scale, postscript) {
  paste('region', paste(scale, 'km', sep = ''), postscript, sep = '_')  
}

scale_parameters <- function(postscript) {
  c(scale_parameter_name(20, postscript), scale_parameter_name(50, postscript), scale_parameter_name(100, postscript))
}

scales_parameters_without <- function(scale_to_exclude, postscript) {
  scales <- scale_parameters(postscript)
  scales[scales != scale_parameter_name(scale_to_exclude, postscript)]
}

select_scales <- function(urban, cultivated, elevation_delta, mean_elevation, average_pop_density, includes_estuary, ssm, susm, ndvi, percentage_protected) {
  append(
    append(
      append(
        append(
          scales_parameters_without(scale_to_exclude = urban, postscript = 'urban'),
          scales_parameters_without(scale_to_exclude = cultivated, postscript = 'cultivated')
        ),
        append(
          scales_parameters_without(scale_to_exclude = elevation_delta, postscript = 'elevation_delta'),
          scales_parameters_without(scale_to_exclude = mean_elevation, postscript = 'mean_elevation')
        )
      ),
      append(
        append(
          scales_parameters_without(scale_to_exclude = average_pop_density, postscript = 'average_pop_density'),
          scales_parameters_without(scale_to_exclude = includes_estuary, postscript = 'includes_estuary')
        ),
        append(
          scales_parameters_without(scale_to_exclude = ssm, postscript = 'ssm'),
          scales_parameters_without(scale_to_exclude = susm, postscript = 'susm')
        )
      )
    ),
    append(
      scales_parameters_without(scale_to_exclude = ndvi, postscript = 'ndvi'),
      scales_parameters_without(scale_to_exclude = percentage_protected, postscript = 'percentage_protected')
    )
  )
}
```

```{r}
select_scales(urban = 20, cultivated = 100, elevation_delta = 20, mean_elevation = 100, average_pop_density = NA, includes_estuary = NA, ssm = 20, susm = 20, ndvi = 100, percentage_protected = NA)
```

select_scales(urban = , cultivated = , elevation_delta = , mean_elevation = , average_pop_density = , includes_estuary = , ssm = , susm = , ndvi =, percentage_protected = )

```{r}
select_variables_from_random_forest(merlin_city_data_fixed)
```

```{r}
exclude_merlin <- !names(merlin_city_data_fixed) %in% select_scales(urban = 20, cultivated = 100, elevation_delta = 50, mean_elevation = 20, average_pop_density = 50, includes_estuary = NA, ssm = 100, susm = 50, ndvi = 20, percentage_protected = 50)

merlin_city_data_fixed_single_scale <- merlin_city_data_fixed[,exclude_merlin]
merlin_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(merlin_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density", "region_50km_average_pop_density")])
create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "biome_name", "realm", "region_100km_ssm", "temperature_annual_average", "temperature_monthly_min", "region_50km_elevation_delta",  "rainfall_monthly_min", "permanent_water", "region_50km_susm", "region_20km_ndvi", "region_20km_urban", "shrubs",  "city_gdp_per_population", "happiness_positive_effect", "city_percentage_protected", "region_100km_cultivated", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_ndvi", "temperature_monthly_max", "city_average_pop_density", "region_50km_average_pop_density", "rainfall_annual_average")])
```

"merlin_pool_size", "biome_name", "realm"


```{r}
birdlife_city_data <- fetch_city_data_for('birdlife')
birdlife_city_data
```

```{r}
ggplot(birdlife_city_data, aes(response)) + geom_histogram(binwidth = 1)
```

```{r}
birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)
birdlife_city_data_fixed
```

```{r}
select_variables_from_random_forest(birdlife_city_data_fixed)
```

```{r}
exclude_birdlife <- !names(birdlife_city_data_fixed) %in% select_scales(urban = 100, cultivated = 100, elevation_delta = 20, mean_elevation = 100, average_pop_density = 20, includes_estuary = NA, ssm = 50, susm = 100, ndvi = 100, percentage_protected = 100)

birdlife_city_data_fixed_single_scale <- birdlife_city_data_fixed[,exclude_birdlife]
birdlife_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm", "region_100km_ndvi")])
create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "region_50km_ssm", "birdlife_pool_size", "biome_name", "region_100km_cultivated", "city_ndvi", "temperature_monthly_min", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "region_100km_susm", "region_20km_average_pop_density", "city_ssm", "permanent_water", "rainfall_monthly_max", "region_100km_urban", "temperature_annual_average", "percentage_urban_area_as_open_public_spaces_and_streets", "region_20km_elevation_delta", "share_of_population_within_400m_of_open_space", "shrubs", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "percentage_urban_area_as_streets", "rainfall_annual_average", "city_susm", "region_100km_ndvi", "happiness_future_life")])

```

"population_growth", "region_50km_ssm", "birdlife_pool_size"



------------------------------------------
So....
------------------------------------------
Merlin: "merlin_pool_size", "biome_name", "realm"
Birdlife: "population_growth", "region_50km_ssm", "birdlife_pool_size"


```{r}
ggplot(merlin_city_data_fixed, aes(x = merlin_pool_size, y = response, color = realm)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
```

```{r}
ggplot(merlin_city_data_fixed, aes(x = merlin_pool_size, y = response, color = biome_name)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
```

```{r}
ggplot(birdlife_city_data_fixed, aes(x = birdlife_pool_size, y = response, color = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
```

```{r}
ggplot(birdlife_city_data_fixed, aes(x = birdlife_pool_size, y = response, color = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F) + theme(legend.position = "bottom")
```

```{r}
ggplot(merlin_city_data_fixed, aes(y = response, x = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F)
```

```{r}
ggplot(birdlife_city_data_fixed, aes(y = response, x = population_growth)) + geom_point() + geom_smooth(method = "glm", se = F)
```
```{r}
ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F)
```

```{r}
ggplot(birdlife_city_data_fixed, aes(y = response, x = region_50km_ssm)) + geom_point() + geom_smooth(method = "glm", se = F)
```

-----------------------------
Try Modelling
-----------------------------

```{r}
library(boot)
```

```{r}
merlin_city_data_fixed_no_boreal <- merlin_city_data_fixed[merlin_city_data_fixed$biome_name != 'Boreal Forests/Taiga',]
birdlife_city_data_fixed_no_boreal <- birdlife_city_data_fixed[birdlife_city_data_fixed$biome_name != 'Boreal Forests/Taiga',]
```

```{r}
test_model <- function(data, formula) {
  fit <- glm(formula, data = data)
  
  cv.glm(data, fit)$delta

  print(paste("R2", with(summary(fit), 1 - deviance/null.deviance)))
  print(paste("CV Delta", cv.glm(data, fit)$delta))
}
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + realm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + biome_name)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + biome_name)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + biome_name + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + biome_name + realm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth)
```
```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + biome_name + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + biome_name + realm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + realm)
```

```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm + biome_name)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + region_50km_ssm + biome_name)
```


```{r}
AIC(
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + biome_name),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + biome_name + realm),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + biome_name),
  glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + region_50km_ssm + realm)
)
```
```{r}
AIC(
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + realm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + biome_name),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + biome_name + realm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + region_50km_ssm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm),
  glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
)
```
```{r}
merlin_city_data_named <- fetch_city_data_for('merlin', T)
birdlife_city_data_named <- fetch_city_data_for('birdlife', T)
```

```{r}
merlin.fit <- glm(data = merlin_city_data_fixed, formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
plot(merlin.fit)
```
```{r}
jpeg("city_effect_merlin_model.jpg")
par(mfrow=c(2, 2))
plot(merlin.fit)
dev.off()
```

```{r}
merlin_city_data_fixed[c(24, 30, 31),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "merlin_pool_size")]
```

```{r}
birdlife_city_data_fixed[c(24, 30, 31),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "birdlife_pool_size")]
```

```{r}
merlin_city_data_named$name[c(24, 30, 31)]
birdlife_city_data_named$name[c(24, 30, 31)]
```

```{r}
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Mediterranean Forests, Woodlands & Scrub'])
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Palearctic'])
```

```{r}
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Indomalayan'])
```

```{r}
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
mean(merlin_city_data_fixed$merlin_pool_size[merlin_city_data_fixed$realm == 'Neotropic'])
```


```{r}
test_model(merlin_city_data_fixed[-c(24, 30, 31, 113),], response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

```{r}
birdlife.fit <- glm(data = birdlife_city_data_fixed, formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
plot(birdlife.fit)
```

```{r}
jpeg("city_effect_birdlife_model.jpg")
par(mfrow=c(2, 2))
plot(birdlife.fit)
dev.off()
```

```{r}
birdlife_city_data_fixed[c(16, 30, 53),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "birdlife_pool_size")]
```
```{r}
merlin_city_data_fixed[c(16, 30, 53),c("response", "biome_name", "realm", "region_50km_ssm", "population_growth", "merlin_pool_size")]
```

```{r}
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Temperate Broadleaf & Mixed Forests'])
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Palearctic'])
```

```{r}
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Indomalayan'])
```

```{r}
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$biome_name == 'Tropical & Subtropical Dry Broadleaf Forests'])
mean(birdlife_city_data_fixed$birdlife_pool_size[birdlife_city_data_fixed$realm == 'Indomalayan'])
```


```{r}
birdlife_city_data_named$name[c(16, 30, 53)]
```

```{r}
test_model(birdlife_city_data_fixed[-c(16, 30, 53, 113),], response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

----------------------------------------------------------------------------------------------------
But can we order cities based on how good they are for biodiversity?
----------------------------------------------------------------------------------------------------

```{r}
merlin_city_data_fixed$residuals <- resid(merlin.fit)
birdlife_city_data_fixed$residuals <- resid(birdlife.fit)
```

```{r}
ggplot(merlin_city_data_fixed, aes(y = response, x = residuals)) + geom_point() + geom_smooth(method = "lm", se = F)
```

```{r}
cor(merlin_city_data_fixed$response, merlin_city_data_fixed$residuals)
```

```{r}
ggplot(birdlife_city_data_fixed, aes(y = response, x = residuals)) + geom_point() + geom_smooth(method = "lm", se = F)
```
```{r}
cor(birdlife_city_data_fixed$response, birdlife_city_data_fixed$residuals)
```

```{r}
ordered_cities <- data.frame(
  ranked_performance = 1:nrow(merlin_city_data_named),
  merlin_base_response = merlin_city_data_named$name[order(-merlin_city_data$response)],
  birdlife_base_response = merlin_city_data_named$name[order(-birdlife_city_data$response)],
  merlin_model_residuals = merlin_city_data_named$name[order(-merlin_city_data$residuals)],
  birdlife_model_residuals = merlin_city_data_named$name[order(-birdlife_city_data$residuals)]
)
ordered_cities
```

```{r}
write_csv(ordered_cities, "city_effect_residuals.csv")
```

-------------------------------------------
What is going on with the response?
-------------------------------------------
```{r}
library(ggrepel)
```

```{r}
merlin_city_data_fixed$name <- merlin_city_data_named$name
plot_merlin_poolsize <- ggplot(merlin_city_data_fixed, aes(y = response, x = merlin_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Merlin response given pool size")
plot_merlin_poolsize
```

```{r}
birdlife_city_data_fixed$name <- birdlife_city_data_named$name
plot_birdlife_poolsize <- ggplot(birdlife_city_data_fixed, aes(y = response, x = birdlife_pool_size)) + 
  geom_smooth(method = "lm", se = F) + 
  geom_point(aes(color = residuals), size = 4) + 
  geom_label_repel(aes(label = name), size = 4) +
  xlab("Pool Size") + ylab("City  Random Effect Response") +
  guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
  theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
  labs(title = "Birdlife response given pool size")
plot_birdlife_poolsize
```


----------------------------------------------
Summary of models
----------------------------------------------
```{r}
summary(merlin.fit)
```

```{r}
summary(birdlife.fit)
```

----------------------------------------------
Review anovas
----------------------------------------------
```{r}
birdlife.biome.anovoa <- aov(response ~ biome_name, data=birdlife_city_data_fixed)
summary(birdlife.biome.anovoa)
```

```{r}
merlin.biome.anovoa <- aov(response ~ biome_name, data=merlin_city_data_fixed)
summary(merlin.biome.anovoa)
```

```{r}
birdlife.realm.anovoa <- aov(response ~ realm, data=birdlife_city_data_fixed)
summary(birdlife.realm.anovoa)
```

```{r}
merlin.realm.anovoa <- aov(response ~ realm, data=merlin_city_data_fixed)
summary(merlin.realm.anovoa)
```

```{r}
interaction.plot(merlin_city_data_fixed$realm, merlin_city_data_fixed$biome_name, merlin_city_data_fixed$response)

meriin.addative.anova <- aov(response ~ biome_name + realm, data=merlin_city_data_fixed) 
summary(meriin.addative.anova)
```

```{r}
interaction.plot(merlin_city_data_fixed$realm, merlin_city_data_fixed$biome_name, merlin_city_data_fixed$response)

meriin.interaction.anova <- aov(response ~ biome_name * realm, data=merlin_city_data_fixed) 
summary(meriin.interaction.anova)
```


```{r}
ggplot(merlin_city_data_fixed, aes(x = response, y = realm)) + geom_boxplot()
```
```{r}
library("stringr")     
```

```{r, fig.height = 12}
ggplot(merlin_city_data_fixed, aes(x = response, y = biome_name)) + 
  geom_boxplot() + 
  facet_wrap(~ realm, scales = "free") +
  theme(text = element_text(size = 30), legend.text=element_text(size=20)) +
  scale_y_discrete(labels = function(x) str_wrap(x, width = 10))
```
```{r, fig.height = 12}
ggplot(birdlife_city_data_fixed, aes(x = response, y = biome_name)) + 
  geom_boxplot() + 
  facet_wrap(~ realm, scales = "free") +
  theme(text = element_text(size = 30), legend.text=element_text(size=20)) +
  scale_y_discrete(labels = function(x) str_wrap(x, width = 10))
```

```{r}
ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm, color = realm)) + geom_point() + geom_smooth(method = "lm", se = F)
```

```{r}
ggplot(merlin_city_data_fixed, aes(y = response, x = region_50km_ssm, color = biome_name)) + geom_point() + geom_smooth(method = "lm", se = F)
```
```{r}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

```{r}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm)
```

```{r, warning=FALSE}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + population_growth + region_50km_ssm *  biome_name * realm)
```

```{r, warning=FALSE}
test_model(birdlife_city_data_fixed_no_boreal, response ~ birdlife_pool_size + population_growth + region_50km_ssm * biome_name * realm)
```

```{r, warning=FALSE}
test_model(merlin_city_data_fixed_no_boreal, response ~ merlin_pool_size + region_50km_ssm * biome_name * realm)
```


```{r}
min(merlin_city_data$response)
max(merlin_city_data$response)
```

```{r}
min(birdlife_city_data$response)
max(birdlife_city_data$response)
```

-------------------------------------------------------
Maybe the either pool is missing the random pool sizes?
-------------------------------------------------------

```{r}
either_city_data <- fetch_city_data_for('either')
either_city_data
```

```{r}
ggplot(either_city_data, aes(response)) + geom_histogram(binwidth = 1)
```

```{r}
either_city_data_fixed <- rfImpute(response ~ ., either_city_data)
either_city_data_fixed
```

```{r}
select_variables_from_random_forest(either_city_data_fixed)
```

```{r}
exclude_either <- !names(either_city_data_fixed) %in% select_scales(urban = 20, cultivated = 100, elevation_delta = 50, mean_elevation = 50, average_pop_density = 20, includes_estuary = NA, ssm = 50, susm = 100, ndvi = 20, percentage_protected = 20)

either_city_data_fixed_single_scale <- either_city_data_fixed[,exclude_either]
either_city_data_fixed_single_scale
```

```{r}
select_variables_from_random_forest(either_city_data_fixed_single_scale)
```

```{r}
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density", "percentage_urban_area_as_open_public_spaces_and_streets")])
create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density", "region_100km_susm", "city_ssm", "temperature_monthly_min", "shrubs", "region_50km_elevation_delta", "biome_name", "permanent_water", "rainfall_monthly_min", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_open_public_spaces", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "city_average_pop_density", "percentage_urban_area_as_open_public_spaces_and_streets", "happiness_positive_effect")])
```

"either_pool_size", "region_50km_ssm", "population_growth", "region_100km_cultivated", "realm", "region_20km_average_pop_density"

```{r}
test_model(either_city_data_fixed, response ~ either_pool_size)
```

```{r}
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm)
```

```{r}
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth)
```

```{r}
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated)
```
```{r}
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm)
```

```{r}
test_model(either_city_data_fixed, response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm + region_20km_average_pop_density)
```
```{r}
AIC(
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size),
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm),
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm + population_growth),
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated),
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm),
  glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm + region_20km_average_pop_density)
)
```
```{r}
either.fit <- glm(data = either_city_data_fixed, formula = response ~ either_pool_size + region_50km_ssm + population_growth + region_100km_cultivated + realm + region_20km_average_pop_density)
either_city_data_fixed$residuals <- resid(either.fit)

cor(either_city_data_fixed$residuals, either_city_data_fixed$response)
```

```{r}
plot(either.fit)
```
```{r}
either_city_data_fixed[c(16, 30, 53),c("response", "either_pool_size")]
```
```{r}
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Temperate Broadleaf & Mixed Forests'])
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Palearctic'])
```

```{r}
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Tropical & Subtropical Moist Broadleaf Forests'])
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Indomalayan'])
```

```{r}
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$biome_name == 'Tropical & Subtropical Dry Broadleaf Forests'])
mean(either_city_data_fixed$either_pool_size[either_city_data_fixed$realm == 'Indomalayan'])
```


```{r}
birdlife_city_data_named$name[c(16, 30, 53)]
```

```{r}
either.fit.2 <- glm(data = either_city_data_fixed, formula = response ~ either_pool_size + population_growth + region_50km_ssm + biome_name + realm)
plot(either.fit.2)
```

------------------------------------------------------------------
Use cross validation and dropping terms to find best model
------------------------------------------------------------------

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

-- Can we drop one?

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + region_50km_ssm + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + population_growth + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

-- drop populaion_growth (14.30384)

-- can we drop another?
```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + region_50km_ssm + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

-- drop ssm (14.20889)

-- can we drop another?

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + biome_name, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ biome_name + realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

-- drop biome (13.10131)

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(merlin_city_data_fixed_no_boreal, glm(formula = response ~ merlin_pool_size * realm, data = merlin_city_data_fixed_no_boreal))$delta[1]
```

-----------------------------------------------
-- best model with merlin is pool size + realm (CV error 13.10131)
-----------------------------------------------

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

-- can we drop a variable?

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + biome_name, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ population_growth + region_50km_ssm + biome_name + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

-- drop biome (5.492536)

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + region_50km_ssm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + population_growth + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ population_growth + region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

-- drop population growth (5.38609)
```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + region_50km_ssm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ region_50km_ssm + realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

-- drop ssm (5.38033)

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```

```{r}
cv.glm(birdlife_city_data_fixed_no_boreal, glm(formula = response ~ birdlife_pool_size * realm, data = birdlife_city_data_fixed_no_boreal))$delta[1]
```
----------------------------------------------------
-- so best model with birdlife is pool size + realm
----------------------------------------------------

```{r}
ggplot(merlin_city_data_fixed_no_boreal, aes(x = merlin_pool_size, y = response)) + geom_point() + geom_smooth(method = "glm", se = F) + facet_wrap(~ realm)
```
```{r}
ggplot(birdlife_city_data_fixed_no_boreal, aes(x = birdlife_pool_size, y = response)) + geom_point() + geom_smooth(method = "glm", se = F) + facet_wrap(~ realm)
```

```{r}
birdlife.fit <- glm(data = birdlife_city_data_fixed_no_boreal, formula = response ~ birdlife_pool_size + realm)
summary(birdlife.fit)
with(summary(birdlife.fit), 1 - deviance/null.deviance)
plot(birdlife.fit)
```
```{r}
birdlife_city_data_fixed_no_boreal[c(16, 53, 116, 124), c("name", "birdlife_pool_size")]
```


```{r}
ggplot(birdlife_city_data_fixed_no_boreal, aes(x = birdlife_pool_size, y = response)) + 
  geom_point() + 
  geom_smooth(method = "glm", se = F) +
  geom_text(aes(label = name), data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], size = 3, position = "dodge", vjust = "inward", hjust = "inward", color = "red", angle=-15) +
  geom_point(data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], color = "red") +
  facet_wrap(~ realm) +
  theme_bw()
```

```{r}
merlin.fit <- glm(data = merlin_city_data_fixed_no_boreal, formula = response ~ merlin_pool_size + realm)
summary(merlin.fit)
with(summary(merlin.fit), 1 - deviance/null.deviance)
plot(merlin.fit)
```
```{r}
city_data[c(7, 24, 77), c("name", "birdlife_pool_size", "merlin_pool_size")]
```
```{r}
ggplot(merlin_city_data_fixed_no_boreal, aes(x = merlin_pool_size, y = response)) + 
  geom_point(size = 1) + 
  geom_smooth(method = "glm", se = F) +
  geom_text(aes(label = name), data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], size = 3, position = "dodge", vjust = "inward", hjust = "inward", color = "red", angle=-15) +
  geom_point(data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], color = "red") +
  facet_wrap(~ realm) +
  theme_bw()
```
```{r}
merlin_city_data_fixed_no_boreal$residuals <- resid(merlin.fit)
ggplot(merlin_city_data_fixed_no_boreal, aes(y = response, x = residuals)) + 
  geom_smooth(method = "lm", se = F) +
  geom_point(aes(color = realm)) + 
  geom_text(aes(label = name), data = merlin_city_data_fixed_no_boreal[c(7, 24, 77),], size = 4, position = "dodge", vjust = "inward", hjust = "inward") + 
  labs(title = "Merlin", subtitle = paste("Correlation", cor(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$response))) +
  theme_bw()
```
```{r}
birdlife_city_data_fixed_no_boreal$residuals <- resid(birdlife.fit)
ggplot(birdlife_city_data_fixed_no_boreal, aes(y = response, x = residuals)) + 
  geom_smooth(method = "lm", se = F, alpha = 0.5) +
  geom_point(aes(color = realm)) + 
  geom_text(aes(label = name), data = birdlife_city_data_fixed_no_boreal[c(16, 53, 124),], size = 4, position = "dodge", vjust = "inward", hjust = "inward") +
  labs(title = "Birdlife", subtitle = paste("Correlation", cor(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$response))) +
  theme_bw()
```
```{r}
birdlife_city_data_fixed_no_boreal$residuals_of_fit <- resid(lm(response ~ residuals, birdlife_city_data_fixed_no_boreal))
ggplot(birdlife_city_data_fixed_no_boreal, aes(x = realm, y = residuals_of_fit)) + geom_boxplot()
```
```{r}
birdlife.aov <- aov(birdlife_city_data_fixed_no_boreal$residuals_of_fit ~ birdlife_city_data_fixed_no_boreal$realm)
summary(birdlife.aov)
```

```{r}
merlin_city_data_fixed_no_boreal$residuals_of_fit <- resid(lm(response ~ residuals, merlin_city_data_fixed_no_boreal))
ggplot(merlin_city_data_fixed_no_boreal, aes(x = realm, y = residuals_of_fit)) + geom_boxplot()
```
```{r}
merlin.aov <- aov(merlin_city_data_fixed_no_boreal$residuals_of_fit ~ merlin_city_data_fixed_no_boreal$realm)
summary(merlin.aov)
```


```{r}
ggplot(merlin_city_data_fixed_no_boreal, aes(residuals)) + geom_histogram(binwidth = 1)
```
```{r}
ggplot(birdlife_city_data_fixed_no_boreal, aes(residuals)) + geom_histogram(binwidth = 1)
```
```{r}
shapiro.test(birdlife_city_data_fixed_no_boreal$response)
shapiro.test(birdlife_city_data_fixed_no_boreal$residuals)
shapiro.test(birdlife_city_data_fixed_no_boreal$residuals_of_fit)

shapiro.test(merlin_city_data_fixed_no_boreal$response)
shapiro.test(merlin_city_data_fixed_no_boreal$residuals)
shapiro.test(merlin_city_data_fixed_no_boreal$residuals_of_fit)
```

Variances of groups are NOT significantly different:
```{r}
bartlett.test(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$realm) 
leveneTest(merlin_city_data_fixed_no_boreal$residuals, merlin_city_data_fixed_no_boreal$realm) 
```

Variances of groups are NOT significantly different:
```{r}
bartlett.test(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$realm) 
leveneTest(birdlife_city_data_fixed_no_boreal$residuals, birdlife_city_data_fixed_no_boreal$realm) 
```

Variances of groups are significantly different:
```{r}
bartlett.test(merlin_city_data_fixed_no_boreal$residuals_of_fit, merlin_city_data_fixed_no_boreal$realm) 
leveneTest(merlin_city_data_fixed_no_boreal$residuals_of_fit, merlin_city_data_fixed_no_boreal$realm) 
```

Variances of groups are significantly different:
```{r}
bartlett.test(birdlife_city_data_fixed_no_boreal$residuals_of_fit, birdlife_city_data_fixed_no_boreal$realm) 
leveneTest(birdlife_city_data_fixed_no_boreal$residuals_of_fit, birdlife_city_data_fixed_no_boreal$realm) 
```

```{r}
unique(merlin_city_data_fixed_no_boreal$realm)
```

```{r, warning = F}
realms <- unique(merlin_city_data_fixed_no_boreal$realm)

for(i in 1:length(realms)) {
  realm <- realms[i]
  delta <- cv.glm(
              data = merlin_city_data_fixed_no_boreal[merlin_city_data_fixed_no_boreal$realm != realm,], 
              glm(
                data = merlin_city_data_fixed_no_boreal[merlin_city_data_fixed_no_boreal$realm != realm,],
                formula = response ~ merlin_pool_size + realm
              )
           )$delta[1]
  print(paste("Exclude Realm CV", realm, delta))
}
```

```{r, warning = F}
realms <- unique(birdlife_city_data_fixed_no_boreal$realm)

for(i in 1:length(realms)) {
  realm <- realms[i]
  delta <- cv.glm(
              data = birdlife_city_data_fixed_no_boreal[birdlife_city_data_fixed_no_boreal$realm != realm,], 
              glm(
                data = birdlife_city_data_fixed_no_boreal[birdlife_city_data_fixed_no_boreal$realm != realm,],
                formula = response ~ birdlife_pool_size + realm
              )
           )$delta[1]
  print(paste("Exclude Realm CV", realm, delta))
}
```


